Modification of seed cell sampling strategy for landslide susceptibility mapping: an application from the Eastern part of the Gallipoli Peninsula (Canakkale, Turkey)

Abstract

In the literature, different sampling strategies (such as landslide area, seed cell, scarp, point) have been applied to landslide susceptibility mapping studies. Landslide sampling strategy should reflect the pre-failure conditions of failures. Hence, researchers tried to develop sampling methods to provide pre-failure conditions. The main purpose of this study was to modify the seed cell sampling strategy for landslide susceptibility evaluations. Furthermore, landslide susceptibility maps were produced using two different sampling strategies (seed cell and landslide area), and compared in terms of their results. In accordance with these purposes, the eastern part of the Gallipoli Peninsula (Canakkale, Turkey) was selected as the study area in which to apply these two sampling strategies. For the seed cell sampling strategy, different random samplings were prepared by considering different buffer distances and different spatial resolutions. Sensitivity analyses were carried out using the landslide area (with depletion and accumulation zones) and samples and seed cells were acquired from different buffer zones with respect to different resolutions. The spatial performance of the landslide susceptibility maps of both sampling strategies were evaluated using area under the ROC curve (AUC) values. The resulting AUC results obtained from different random samplings and different spatial resolutions indicated that the appropriate buffer distance used to produce seed cells for the evaluation of landslide susceptibility maps in medium scale should be approximately 50 m.

Comparison of Statistical Analysis Models for Susceptibility Assessment of Earthquake-Triggered Landslides: A Case Study from 2015 Earthquake in Lefkada Island

“…A more advanced validation method, which has been widely applied, is the receiver operating characteristics (ROC) analysis [8,15,43,44]. Based on the overlay of landslide susceptibility maps with an "independent" validation dataset (with landslide and non-landslide data), a ROC graph was created.…”

“…The final landslide inventory contained 43 landslide events. Given that in landslide inventories it is not always possible to differentiate their depletion and accumulation zones [15], these zones were mapped together in an entire area forming a single polygon feature for each landslide event (Figure 1).…”

An extreme rainfall-induced landslide susceptibility assessment using autoencoder combined with random forest in Shimane Prefecture, Japan

“…The landslide-affecting factors in study area are complex, and it is difficult to confirm which affecting factors are the most important and necessary among the topographic, geological, hydrological, distance to stream and distance to road. In landslide susceptibility modeling, landslides may reoccur under conditions similar to those of past landslides (Westen et al 2003;Lee and Talib 2005;Dagdelenler et al 2016). A total of 14 affecting factors were acquired and chosen as input variables for landslide susceptibility models (Figs.…”

The performance of using an autoencoder for prediction and susceptibility assessment of landslides: A case study on landslides triggered by the 2018 Hokkaido Eastern Iburi earthquake in Japan

“…The pixel size of the factors that influenced the landslides was set to 10 m × 10 m regardless of the resolution of the original data source (Zhu et al 2018). In landslide susceptibility modeling, a landslide may reoccur under conditions similar to past landslides (Westen et al 2003;Lee and Talib, 2005;Dagdelenler et al 2016). There is no common guideline for selecting the factors that influence landslides (Ayalew and Yamagishi.…”